-------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  /Users/Wei/Dropbox/Fertility/Results/Macro_results.log
  log type:  text
 opened on:  22 Apr 2020, 23:20:22

. 
. *** This do file generates Figure B1, B2, B3 and Table B1 ***
. 
. use "$path2/fines_use", clear 
(Year X Province fertility policy data)

. recode year (1979/1984 = 1) (1985/1989 = 2) (1990/1995 = 3) (1995/1999 = 4)(2000/2005 = 5), gen
> (year_group)
(837 differences between year and year_group)

. tw line fine year if prov != 54, by(province) xtit(Year)

. gr export "$path4/fig_b1.eps", replace 
(file /Users/Wei/Dropbox/Fertility/Figures/fig_b1.eps written in EPS format)

. 
. collapse fine, by(prov year_group)

. reshape wide fine, i(prov) j(year_group)
(note: j = 1 2 3 4 5)

Data                               long   ->   wide
-----------------------------------------------------------------------------
Number of obs.                      155   ->      31
Number of variables                   3   ->       6
j variable (5 values)        year_group   ->   (dropped)
xij variables:
                                   fine   ->   fine1 fine2 ... fine5
-----------------------------------------------------------------------------

. rename prov code

. merge 1:m code using "$path2/china_label.dta"
(note: variable code was float, now double to accommodate using data's values)

    Result                           # of obs.
    -----------------------------------------
    not matched                             4
        from master                         0  (_merge==1)
        from using                          4  (_merge==2)

    matched                                62  (_merge==3)
    -----------------------------------------

. drop if code == 0
(0 observations deleted)

. replace ename = subinstr(ename," Sheng"," ",.) 
(54 real changes made)

. replace ename = subinstr(ename," Shi"," ",.) 
(6 real changes made)

. replace ename = subinstr(ename," Zhizhiqu"," ",.) 
(0 real changes made)

. 
. spmap fine1 using "$path2/china_map.dta" ,id(id) clmethod(custom) fcolor(Blues2) ocolor(none..)
>  ndfcolor(gs15)  clbreaks(0 1.5 2.5 3.5 4.5 5) legend(label(2 "< 1.5") label(6 "> 4.5") subtit(
> "Penalty levels", size(vsmall)))
(note:  named style none.. not found in class color, default attributes used)
(note:  named style none.. not found in class color, default attributes used)

. gr export "$path4/fig_b2a.eps",replace 
(file /Users/Wei/Dropbox/Fertility/Figures/fig_b2a.eps written in EPS format)

. spmap fine2 using "$path2/china_map.dta" ,id(id) clmethod(custom)  fcolor(Blues2) ocolor(none..
> ) ndfcolor(gs15)  clbreaks(0 1.5 2.5 3.5 4.5 5) legend(label(2 "< 1.5") label(6 "> 4.5") subtit
> ("Penalty levels", size(vsmall)))
(note:  named style none.. not found in class color, default attributes used)
(note:  named style none.. not found in class color, default attributes used)

. gr export "$path4/fig_b2b.eps",replace 
(file /Users/Wei/Dropbox/Fertility/Figures/fig_b2b.eps written in EPS format)

. spmap fine3 using "$path2/china_map.dta" ,id(id) clmethod(custom)  fcolor(Blues2) ocolor(none..
> ) ndfcolor(gs15)  clbreaks(0 1.5 2.5 3.5 4.5 5) legend(label(2 "< 1.5") label(6 "> 4.5") subtit
> ("Penalty levels", size(vsmall)))
(note:  named style none.. not found in class color, default attributes used)
(note:  named style none.. not found in class color, default attributes used)

. gr export "$path4/fig_b2c.eps",replace 
(file /Users/Wei/Dropbox/Fertility/Figures/fig_b2c.eps written in EPS format)

. spmap fine4 using "$path2/china_map.dta" ,id(id) clmethod(custom)  fcolor(Blues2) ocolor(none..
> ) ndfcolor(gs15)   clbreaks(0 1.5 2.5 3.5 4.5 5) legend(label(2 "< 1.5") label(6 "> 4.5") subti
> t("Penalty levels", size(vsmall)))
(note:  named style none.. not found in class color, default attributes used)
(note:  named style none.. not found in class color, default attributes used)

. gr export "$path4/fig_b2d.eps",replace 
(file /Users/Wei/Dropbox/Fertility/Figures/fig_b2d.eps written in EPS format)

. spmap fine5 using "$path2/china_map.dta" ,id(id) clmethod(custom)  fcolor(Blues2) ocolor(none..
> ) ndfcolor(gs15) clbreaks(0 1.5 2.5 3.5 4.5 5) legend(label(2 "< 1.5") label(6  "> 4.5") subtit
> ("Penalty levels", size(vsmall)))
(note:  named style none.. not found in class color, default attributes used)
(note:  named style none.. not found in class color, default attributes used)

. gr export "$path4/fig_b2e.eps",replace 
(file /Users/Wei/Dropbox/Fertility/Figures/fig_b2e.eps written in EPS format)

. 
. 
. 
. use "$path2/60years", clear

. drop if mi(prov)
(0 observations deleted)

. merge 1:1 prov year using "$path2/fines_use", nogen 
(note: variable year was int, now float to accommodate using data's values)

    Result                           # of obs.
    -----------------------------------------
    not matched                         1,022
        from master                     1,022  
        from using                          0  

    matched                               837  
    -----------------------------------------

. replace fine = 0 if year <= 1979 & fine == . 
(929 real changes made)

. replace fine = . if year < 1978
(898 real changes made, 898 to missing)

. 
. xtset prov year 
       panel variable:  prov (unbalanced)
        time variable:  year, 1949 to 2008
                delta:  1 unit

. 
. egen cid = group(prov)

. gen lnpic = ln(pic)
(1,433 missing values generated)

. replace lnpic = 0 if lnpic == . & year <= 2000
(1,428 real changes made)

. gen lnmic = ln(mic)
(1,560 missing values generated)

. replace lnmic = 0 if lnmic == . & year <= 2000
(1,549 real changes made)

. gen lnuic = ln(uic)
(1,449 missing values generated)

. replace lnuic = 0 if lnuic == . & year <= 2000
(1,436 real changes made)

. 
. gen lnpop = ln(pop)
(5 missing values generated)

. gen lnpop_male = ln(pop)
(5 missing values generated)

. gen lnpop_female = ln(pop)
(5 missing values generated)

. gen lnpop_urban = ln(pop_urban)
(446 missing values generated)

. gen lnpop_rural = ln(pop_rural)
(446 missing values generated)

. 
. gen lngdp_pc = ln(gdp/pop*10000)
(85 missing values generated)

. gen lngdp1_pc = ln(gdp_1st/pop)
(85 missing values generated)

. gen lngdp2_pc = ln(gdp_2nd/pop)
(85 missing values generated)

. gen lngdp3_pc = ln(gdp_3rd/pop)
(85 missing values generated)

. 
. gen gdp1_share = gdp_1st/gdp
(83 missing values generated)

. gen gdp2_share = gdp_2nd/gdp
(83 missing values generated)

. gen gdp3_share = gdp_3rd/gdp
(83 missing values generated)

. 
. gen lngov_exp = ln(exp_gen)
(93 missing values generated)

.  
. gen lngov_admin = ln(exp_admin+1)
(111 missing values generated)

. gen lngov_ag = ln(exp_ag+1)
(261 missing values generated)

. gen lngov_cul = ln(exp_cul+1)
(153 missing values generated)

. gen lngov_ss = ln(exp_ss+1)
(1,181 missing values generated)

. 
. replace lngov_ss = 0 if lngov_ss==. & year >= 1978
(465 real changes made)

. gen lnwage= ln(wage_total/worker_total)
(380 missing values generated)

. gen lnwage_urban = ln(wage_urban/worker_urban)
(665 missing values generated)

. gen lnwage_state = ln(wage_state/worker_state)
(310 missing values generated)

. 
. gen lnteacher_primary = ln(teacher_primary/pop)
(89 missing values generated)

. gen lnteacher_second = ln(teacher_second/pop)
(96 missing values generated)

. gen lnteacher_higher = ln(teacher_higher/pop)
(79 missing values generated)

. 
. gen lnemp = ln(emp+1)
(236 missing values generated)

. gen lnemp_urban = ln(emp_urban +1)
(227 missing values generated)

. gen lnemp_rural = ln(emp_rural +1)
(303 missing values generated)

. 
. gen lndibao = ln(dibao+1)
(1,487 missing values generated)

. replace lndibao = 0 if year <= 1990 & lndibao == . 
(1,266 real changes made)

. 
. gen treat = 1 if prov == 11 | prov == 13 | prov == 21 | prov == 31 | prov == 32 | prov == 33 | 
> prov == 35 |prov == 41 | prov == 42 | prov == 43 |prov == 44 | prov == 45| prov == 46| prov == 
> 53| prov == 64 | prov == 65
(899 missing values generated)

. replace treat = 0 if mi(treat)
(899 real changes made)

. 
. 
. gen p_val = . 
(1,859 missing values generated)

. local j = 1 

. 
. cap erase "$path3/tab_b1.xls"

. cap erase "$path3/tab_b1.txt"

. xtset prov year 
       panel variable:  prov (unbalanced)
        time variable:  year, 1949 to 2008
                delta:  1 unit

. 
. qui:{
 :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  
> :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  : 
>  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  :
>   :  :  :  :  :  :  :  :  :  :  :  :  :  :  :  : 
. 
. xtset prov year 
       panel variable:  prov (unbalanced)
        time variable:  year, 1949 to 2008
                delta:  1 unit

. 
. foreach y_var in  "lnpop lnpop_urban lnpop_rural" "birth_rate death_rate"  "lnemp lnemp_urban l
> nemp_rural"  "lnwage lnwage_urban lnwage_state" "lngdp_pc unem_rate unem_urban" ///
>   "lnpic lnmic lnuic lndibao" "lngov_exp lngov_admin lngov_ag lngov_cul lngov_ss" ///
> "bed_per10k doc_per10k" "lnteacher_primary lnteacher_second lnteacher_higher"{
  2. reghdfe fine l1.(`y_var') , a(prov##c.year year) cluster(cid)
  3. testparm l1.(`y_var')
  4. replace p_val = `r(p)' in `j' 
  5. local ++j 
  6. outreg2 using "$path3/tab_b1.xls", adds(F-test, `r(F)',p-val, `r(p)')  // col 5
  7. 
. cap drop `y_var'_1p `y_var'_2p `y_var'_3p
  8. }
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        708
Absorbing 2 HDFE groups                           F(   3,     29) =       1.73
Statistics robust to heteroskedasticity           Prob > F        =     0.1824
                                                  R-squared       =     0.8572
                                                  Adj R-squared   =     0.8363
                                                  Within R-sq.    =     0.0374
Number of clusters (cid)     =         30         Root MSE        =     0.5082

                                   (Std. Err. adjusted for 30 clusters in cid)
------------------------------------------------------------------------------
             |               Robust
        fine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lnpop |
         L1. |  -3.052559   4.110661    -0.74   0.464     -11.4598    5.354686
             |
 lnpop_urban |
         L1. |  -.8509015   .4368835    -1.95   0.061    -1.744429    .0426255
             |
 lnpop_rural |
         L1. |  -.8423456   .8914514    -0.94   0.353    -2.665568    .9808772
             |
       _cons |   36.89278   32.39587     1.14   0.264    -29.36421    103.1498
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        prov |        30          30           0    *|
 prov#c.year |        30           0          30     |
        year |        28           0          28    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  L.lnpop = 0
 ( 2)  L.lnpop_urban = 0
 ( 3)  L.lnpop_rural = 0

       F(  3,    29) =    1.73
            Prob > F =    0.1824
(1 real change made)
/Users/Wei/Dropbox/Fertility/Results/tab_b1.xls
dir : seeout
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        865
Absorbing 2 HDFE groups                           F(   2,     30) =       0.22
Statistics robust to heteroskedasticity           Prob > F        =     0.8069
                                                  R-squared       =     0.8463
                                                  Adj R-squared   =     0.8282
                                                  Within R-sq.    =     0.0016
Number of clusters (cid)     =         31         Root MSE        =     0.5005

                                   (Std. Err. adjusted for 31 clusters in cid)
------------------------------------------------------------------------------
             |               Robust
        fine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  birth_rate |
         L1. |   .0030463   .0223527     0.14   0.893     -.042604    .0486966
             |
  death_rate |
         L1. |  -.0496363    .077933    -0.64   0.529    -.2087968    .1095242
             |
       _cons |   1.976551   .4701644     4.20   0.000     1.016347    2.936755
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        prov |        31          31           0    *|
 prov#c.year |        31           0          31     |
        year |        28           0          28    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  L.birth_rate = 0
 ( 2)  L.death_rate = 0

       F(  2,    30) =    0.22
            Prob > F =    0.8069
(1 real change made)
/Users/Wei/Dropbox/Fertility/Results/tab_b1.xls
dir : seeout
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =        841
Absorbing 2 HDFE groups                           F(   3,     30) =       1.47
Statistics robust to heteroskedasticity           Prob > F        =     0.2430
                                                  R-squared       =     0.8483
                                                  Adj R-squared   =     0.8297
                                                  Within R-sq.    =     0.0241
Number of clusters (cid)     =         31         Root MSE        =     0.5004

                                   (Std. Err. adjusted for 31 clusters in cid)
------------------------------------------------------------------------------
             |               Robust
        fine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lnemp |
         L1. |   1.784305   1.039937     1.72   0.097    -.3395294     3.90814
             |
 lnemp_urban |
         L1. |   .0027042   .0480727     0.06   0.956    -.0954733    .1008817
             |
 lnemp_rural |
         L1. |  -2.107045   1.112274    -1.89   0.068    -4.378612    .1645218
             |
       _cons |   3.145534   6.103513     0.52   0.610    -9.319504    15.61057
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        prov |        31          31           0    *|
 prov#c.year |        31           0          31     |
        year |        28           0          28    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  L.lnemp = 0
 ( 2)  L.lnemp_urban = 0
 ( 3)  L.lnemp_rural = 0

       F(  3,    30) =    1.47
            Prob > F =    0.2430
(1 real change made)
/Users/Wei/Dropbox/Fertility/Results/tab_b1.xls
dir : seeout
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        825
Absorbing 2 HDFE groups                           F(   3,     30) =       1.87
Statistics robust to heteroskedasticity           Prob > F        =     0.1555
                                                  R-squared       =     0.8447
                                                  Adj R-squared   =     0.8252
                                                  Within R-sq.    =     0.0183
Number of clusters (cid)     =         31         Root MSE        =     0.5040

                                   (Std. Err. adjusted for 31 clusters in cid)
------------------------------------------------------------------------------
             |               Robust
        fine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      lnwage |
         L1. |   1.585713   .7318265     2.17   0.038     .0911235    3.080302
             |
lnwage_urban |
         L1. |  -.5649582   .2709583    -2.09   0.046    -1.118329   -.0115876
             |
lnwage_state |
         L1. |  -.4834365   .5282766    -0.92   0.367    -1.562321    .5954482
             |
       _cons |    1.61986    .947017     1.71   0.097    -.3142066    3.553927
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        prov |        31          31           0    *|
 prov#c.year |        31           0          31     |
        year |        28           0          28    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  L.lnwage = 0
 ( 2)  L.lnwage_urban = 0
 ( 3)  L.lnwage_state = 0

       F(  3,    30) =    1.87
            Prob > F =    0.1555
(1 real change made)
/Users/Wei/Dropbox/Fertility/Results/tab_b1.xls
dir : seeout
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =        710
Absorbing 2 HDFE groups                           F(   3,     29) =       2.12
Statistics robust to heteroskedasticity           Prob > F        =     0.1189
                                                  R-squared       =     0.8406
                                                  Adj R-squared   =     0.8174
                                                  Within R-sq.    =     0.0137
Number of clusters (cid)     =         30         Root MSE        =     0.5220

                                   (Std. Err. adjusted for 30 clusters in cid)
------------------------------------------------------------------------------
             |               Robust
        fine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
    lngdp_pc |
         L1. |    .502054   .5764347     0.87   0.391    -.6768873    1.680995
             |
   unem_rate |
         L1. |   .0795573    .033544     2.37   0.025     .0109521    .1481625
             |
  unem_urban |
         L1. |  -.0117853   .0095216    -1.24   0.226    -.0312592    .0076886
             |
       _cons |   -2.06053   4.458862    -0.46   0.647    -11.17993    7.058867
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        prov |        30          30           0    *|
 prov#c.year |        30           0          30     |
        year |        28           0          28    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  L.lngdp_pc = 0
 ( 2)  L.unem_rate = 0
 ( 3)  L.unem_urban = 0

       F(  3,    29) =    2.12
            Prob > F =    0.1189
(1 real change made)
/Users/Wei/Dropbox/Fertility/Results/tab_b1.xls
dir : seeout
(MWFE estimator converged in 7 iterations)

HDFE Linear regression                            Number of obs   =        646
Absorbing 2 HDFE groups                           F(   4,     30) =       0.80
Statistics robust to heteroskedasticity           Prob > F        =     0.5354
                                                  R-squared       =     0.8874
                                                  Adj R-squared   =     0.8684
                                                  Within R-sq.    =     0.0201
Number of clusters (cid)     =         31         Root MSE        =     0.4241

                                   (Std. Err. adjusted for 31 clusters in cid)
------------------------------------------------------------------------------
             |               Robust
        fine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
       lnpic |
         L1. |  -.0428946   .0555045    -0.77   0.446    -.1562499    .0704607
             |
       lnmic |
         L1. |  -.0132999   .0453068    -0.29   0.771    -.1058287     .079229
             |
       lnuic |
         L1. |    .013888   .0430267     0.32   0.749    -.0739842    .1017602
             |
     lndibao |
         L1. |   .1476417   .0971066     1.52   0.139    -.0506764    .3459598
             |
       _cons |   1.507991   .1060486    14.22   0.000     1.291411    1.724572
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        prov |        31          31           0    *|
 prov#c.year |        31           0          31     |
        year |        28           0          28    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  L.lnpic = 0
 ( 2)  L.lnmic = 0
 ( 3)  L.lnuic = 0
 ( 4)  L.lndibao = 0

       F(  4,    30) =    0.80
            Prob > F =    0.5354
(1 real change made)
/Users/Wei/Dropbox/Fertility/Results/tab_b1.xls
dir : seeout
(MWFE estimator converged in 6 iterations)

HDFE Linear regression                            Number of obs   =        768
Absorbing 2 HDFE groups                           F(   5,     30) =       0.73
Statistics robust to heteroskedasticity           Prob > F        =     0.6079
                                                  R-squared       =     0.8460
                                                  Adj R-squared   =     0.8245
                                                  Within R-sq.    =     0.0231
Number of clusters (cid)     =         31         Root MSE        =     0.5097

                                   (Std. Err. adjusted for 31 clusters in cid)
------------------------------------------------------------------------------
             |               Robust
        fine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
   lngov_exp |
         L1. |  -.3582305   .4390258    -0.82   0.421    -1.254841    .5383797
             |
 lngov_admin |
         L1. |   .2774425    .359798     0.77   0.447     -.457363    1.012248
             |
    lngov_ag |
         L1. |  -.1284591   .1149367    -1.12   0.273    -.3631911     .106273
             |
   lngov_cul |
         L1. |    .595143   .7369916     0.81   0.426    -.9099946    2.100281
             |
    lngov_ss |
         L1. |  -.0856322   .1364035    -0.63   0.535    -.3642052    .1929409
             |
       _cons |   1.279238   1.568279     0.82   0.421    -1.923615    4.482091
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        prov |        31          31           0    *|
 prov#c.year |        31           0          31     |
        year |        28           0          28    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  L.lngov_exp = 0
 ( 2)  L.lngov_admin = 0
 ( 3)  L.lngov_ag = 0
 ( 4)  L.lngov_cul = 0
 ( 5)  L.lngov_ss = 0

       F(  5,    30) =    0.73
            Prob > F =    0.6079
(1 real change made)
/Users/Wei/Dropbox/Fertility/Results/tab_b1.xls
dir : seeout
(MWFE estimator converged in 4 iterations)

HDFE Linear regression                            Number of obs   =        858
Absorbing 2 HDFE groups                           F(   2,     30) =       0.41
Statistics robust to heteroskedasticity           Prob > F        =     0.6648
                                                  R-squared       =     0.8455
                                                  Adj R-squared   =     0.8272
                                                  Within R-sq.    =     0.0053
Number of clusters (cid)     =         31         Root MSE        =     0.5014

                                   (Std. Err. adjusted for 31 clusters in cid)
------------------------------------------------------------------------------
             |               Robust
        fine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
  bed_per10k |
         L1. |   .0217219   .0277629     0.78   0.440    -.0349774    .0784213
             |
  doc_per10k |
         L1. |   .0039486   .0157963     0.25   0.804    -.0283118     .036209
             |
       _cons |   1.097231   .7032577     1.56   0.129    -.3390132    2.533475
------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        prov |        31          31           0    *|
 prov#c.year |        31           0          31     |
        year |        28           0          28    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  L.bed_per10k = 0
 ( 2)  L.doc_per10k = 0

       F(  2,    30) =    0.41
            Prob > F =    0.6648
(1 real change made)
/Users/Wei/Dropbox/Fertility/Results/tab_b1.xls
dir : seeout
(MWFE estimator converged in 5 iterations)

HDFE Linear regression                            Number of obs   =        857
Absorbing 2 HDFE groups                           F(   3,     30) =       0.44
Statistics robust to heteroskedasticity           Prob > F        =     0.7239
                                                  R-squared       =     0.8464
                                                  Adj R-squared   =     0.8279
                                                  Within R-sq.    =     0.0112
Number of clusters (cid)     =         31         Root MSE        =     0.5003

                                        (Std. Err. adjusted for 31 clusters in cid)
-----------------------------------------------------------------------------------
                  |               Robust
             fine |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
lnteacher_primary |
              L1. |   .2187659   .6966404     0.31   0.756    -1.203964    1.641495
                  |
 lnteacher_second |
              L1. |  -.3066692   .4059224    -0.76   0.456    -1.135673    .5223349
                  |
 lnteacher_higher |
              L1. |   .4739329    .484591     0.98   0.336     -.515734      1.4636
                  |
            _cons |   1.335529   2.722103     0.49   0.627    -4.223747    6.894805
-----------------------------------------------------------------------------------

Absorbed degrees of freedom:
-----------------------------------------------------+
 Absorbed FE | Categories  - Redundant  = Num. Coefs |
-------------+---------------------------------------|
        prov |        31          31           0    *|
 prov#c.year |        31           0          31     |
        year |        28           0          28    ?|
-----------------------------------------------------+
? = number of redundant parameters may be higher
* = FE nested within cluster; treated as redundant for DoF computation

 ( 1)  L.lnteacher_primary = 0
 ( 2)  L.lnteacher_second = 0
 ( 3)  L.lnteacher_higher = 0

       F(  3,    30) =    0.44
            Prob > F =    0.7239
(1 real change made)
/Users/Wei/Dropbox/Fertility/Results/tab_b1.xls
dir : seeout

. 
. 
. 
. hist p_val, kdens xtit("P-values") xline(0.1, lp(dash)) xline(0.05, lp(solid)) xlabel(0.05 0(0.
> 1)1)
(bin=11, start=.10126845, width=.08107791)

. gr export "$path4/fig_b3.eps",replace 
(file /Users/Wei/Dropbox/Fertility/Figures/fig_b3.eps written in EPS format)

. su p_val, d

                            p_val
-------------------------------------------------------------
      Percentiles      Smallest
 1%     .1078326       .1012684
 5%     .1379009       .1078326
10%     .1582897       .1184954       Obs                 121
25%     .2752319       .1189148       Sum of Wgt.         121

50%     .4455711                      Mean           .5016432
                        Largest       Std. Dev.      .2706926
75%      .745356       .9735402
90%     .8820959       .9818349       Variance       .0732745
95%     .9173805       .9922371       Skewness        .296062
99%     .9922371       .9931254       Kurtosis       1.725873

. 
. log close 
      name:  <unnamed>
       log:  /Users/Wei/Dropbox/Fertility/Results/Macro_results.log
  log type:  text
 closed on:  22 Apr 2020, 23:22:07
-------------------------------------------------------------------------------------------------
